Deep learning methods have dramatically improved the state of the art in many other domains such as drug discovery and genomics.

True if published results as of May 2015 show deep learning methods markedly improving the state of the art in drug discovery (for example the Merck Kaggle challenge, Tox21) and in genomics (for example predicting splicing or the effects of mutations), as well as in other domains. Mostly true if clear improvements are shown in both but 'dramatically' overstates the gains in one of them. Mixed if clear state-of-the-art gains are documented for only one of drug discovery and genomics. Mostly false if the evidence by then amounts to only early or modest results in both, stated as dramatic. False if deep learning had not improved the state of the art in either field by then.

Set before the research began.

1. E1 Toxicity Prediction using Deep Learning arXiv (ar5iv rendering); authors at Institute of Bioinformatics, Johannes Kepler University Linz

   “Though Deep Learning is new to the field and was never applied to toxicity prediction before, it clearly outperformed all other participating methods.”

2. E2 Deep Neural Nets as a Method for Quantitative Structure−Activity Relationships Journal of Chemical Information and Modeling (Ma, Svetnik, Sheridan, Dahl, Liaw; Merck Research Laboratories and University of Toronto)

   “Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck’s drug discovery effort.”

3. E3 Deep Neural Nets as a Method for Quantitative Structure−Activity Relationships Journal of Chemical Information and Modeling (Ma, Svetnik, Sheridan, Dahl, Liaw; Merck Research Laboratories and University of Toronto)

   “The winning entry (submitted by one of the authors, George Dahl) improved the mean R2 averaged over the 15 data sets from 0.42 (for RF) to 0.49.”

4. E4 Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning - Nature Biotechnology Nature Biotechnology · 27 July 2015

   “Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data.”

For drug discovery, the evidence shows clear gains. The toxicity prediction paper (E1, arXiv 1503, which is the March 2015 numbering) says deep learning "clearly outperformed all other participating methods" in toxicity prediction. The Merck QSAR paper (E2, E3) reports that deep neural nets routinely beat random forests, and that the winning Kaggle entry raised mean R² from 0.42 to 0.49, a clear but moderate gain. The only genomics evidence (E4) was published on 2015-07-27, after the 2015-05-27 judge date, so the evidence does not document genomics gains as of the claim date. Since state-of-the-art gains are documented for only one of the two named fields, this falls under the criteria's 'mixed' tier.